Bowen Wang

Dr. Bowen Wang

Tongji University, Shanghai, China
orcid

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 June 2022 in Sensors
Verified
Journal Article
Open access
PDF file
FLFeng Luo
Feng Luo
BWBowen Wang
Bowen Wang
ZYzhenyu yang
zhenyu yang
PZPing Zhang
Ping Zhang
YMYifei Ma
Yifei Ma
ZSZhipeng Sun
Zhipeng Sun
Advances in automotive technology require networks to support a variety of communication requirements, such as reliability, real-time performance, low jitter, and strict delay limits. Time-Sensitive Network (TSN) is a keyframe transmission delay-guaranteed solution based on the IEEE 802 architecture of the automotive Ethernet. However, most of the existing studies on automotive TSN performance are based on a single mechanism, lacking a complete and systematic research tool. At the same time, the design method should be considered from a global perspective when designing an automotive TSN system, rather than only considering a single mechanism that TSN applies to. This paper discusses the correspondence between traffic types and automotive scenarios and proposes a methodology to target the delay constraint of traffic types as the design goal of automotive TSN networks. To study the performance of automotive TSN under different mechanisms such as time-aware shaper (TAS), credit-based shaper (CBS), cyclic queuing and forwarding (CQF), etc., this paper also develops a systematic automotive TSN simulation system based on OMNeT++. The simulation system plays a crucial role in the whole methodology, including all applicable TSN standards for the automotive field. Lastly, a complex automotive scenario based on zonal architecture provided by a major motor company in Shanghai is analyzed in the simulated system; verifying TSN can guarantee real-time performance and reliability of the in-vehicle network.

28 April 2023 in Sensors
Verified
Journal Article
Open access
PDF file
FLFeng Luo
Feng Luo
ZYzhenyu yang
zhenyu yang
ZZZhaojing Zhang
Zhaojing Zhang
ZWZitong Wang
Zitong Wang
BWBowen Wang
Bowen Wang
MWmingzhi wu
mingzhi wu
The automotive Ethernet is gradually replacing the traditional controller area network (CAN) as the backbone network of the vehicle. As an essential protocol to solve service-based communication, Scalable service-Oriented MiddlewarE over IP (SOME/IP) is expected to be applied to an in-vehicle network (IVN). The increasing number of external attack interfaces and the protocol’s vulnerability makes SOME/IP in-vehicle networks vulnerable to intrusion. This paper proposes a multi-layer intrusion detection system (IDS) architecture, including rule-based and artificial intelligence (AI)-based modules. The rule-based module is used to detect the SOME/IP header, SOME/IP-SD message, message interval, and communication process. The AI-based module acts on the payload. We propose a SOME/IP dataset establishment method to evaluate the performance of the proposed multi-layer IDS. Experiments are carried out on a Jetson Xavier NX, showing that the accuracy of AI-based detection reached 99.7761% and that of rule-based detection was 100%. The average detection time per packet is 0.3958 ms with graphics processing unit (GPU) acceleration and 0.6669 ms with only a central processing unit (CPU). After vehicle-level real-time analyses, the proposed IDS can be deployed for distributed or select critical advanced driving assistance system (ADAS) traffic for detection in a centralized layout.

24 September 2021 in Security and Communication Networks
Verified
Journal Article
Open access
PDF file
FLFeng Luo
Feng Luo
BWBowen Wang
Bowen Wang
ZFZihao Fang
Zihao Fang
ZYzhenyu yang
zhenyu yang
Yifan JiangYifan Jiang
Yifan Jiang
With the development of intelligent and connected vehicles, onboard Ethernet will play an important role in the next generation of vehicle network architectures. It is well established that accurate timing and guaranteed data delivery are critical in the automotive environment. The time-sensitive network (TSN) protocol can precisely guarantee the time certainty of the key signals of automotive Ethernet. With the time-sensitive network based on automotive Ethernet being standardized by the TSN working group, the TSN has already entered the vision of the automotive network. However, the security mechanism of the TSN protocol is rarely discussed. First, the security of the TSN automotive Ethernet as a backbone E/E (electrical/electronic) architecture is analyzed in this paper through the Microsoft STRIDE threat model, and possible countermeasures for the security of automotive TSNs are listed, including the security protocol defined in the TSN, so that the TSN security protocol and the traditional protection technology can form a complete automotive Ethernet protection system. Then, the security mechanism per-stream filtering and policing (PSFP) defined in IEEE 802.1Qci is analyzed in detail, and an anomaly detection system based on PSFP is proposed in this paper. Finally, OMNeT++ is used to simulate a real TSN topology to evaluate the performance of the proposed anomaly detection system (ADS). As a result, the protection strategy based on 802.1Qci not only ensures the real-time performance of the TSN but can also isolate individuals with abnormal behavior and block DoS (denial of service) attacks, thus attaining the security protection of the TSN vehicle-based network.

25 November 2021
Verified
Conference Paper
Closed access
DDDandan Ding
Dandan Ding
FBFanliang Bu
Fanliang Bu
BWBowen Wang
Bowen Wang
This paper introduces the characteristics and applications of Ad Hoc networks, gives two typical reactive routing protocols routing process of AODV and DSR, models and simulates them with the help of OPNET [1] software, verifies the relevant attributes of AODV and DSR routing protocols through the comparative analysis of simulation results, explores the effect of different number of nodes on the number of hops routed in self-organized networks, routing discovery time, network delay and load and other attributes performance of different number of nodes are explored and qualitative analysis is given, which provides reference for subsequent in-depth research.